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A Multi-User Personal Indoor Localization System Employing graph-based optimization

SENSORS 2019年第1期19卷 157-157页

作者： Nowicki, Michal R. Skrzypczynski, Piotr Poznan Univ Tech Inst Control Robot & Informat Engn PL-60965 Poznan Poland

Personal indoor localization with smartphones is a well-researched area, with a number of approaches solving the problem separately for individual users. Most commonly, a particle filter is used to fuse information from dead reckoning and WiFi or Bluetooth adapters to provide an accurate location of the person holding a smartphone. Unfortunately, the existing solutions largely ignore the gains that emerge when a single localization system estimates locations of multiple users in the same environment. Approaches based on filtration maintain only estimates of the current poses of the users, marginalizing the historical data. Therefore, it is difficult to fuse data from multiple individual trajectories that are usually not perfectly synchronized in time. We propose a system that fuses the information from WiFi and dead reckoning employing the graph-based optimization, which is widely applied in robotics. The presented system can be used for localization of a single user, but the improvement is especially visible when this approach is extended to a multi-user scenario. The article presents a number of experiments performed with a smartphone inside an office building. These experiments demonstrate that graph-based optimization can be used as an efficient fusion mechanism to obtain accurate trajectory estimates both in the case of a single user and in a multi-user indoor localization system. The code of our system together with recorded dataset will be made available when the paper gets published.

关键词： indoor positioning graph-based optimization WiFi fingerprinting

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Fusion of Extreme Learning Machine and graph-based optimization Methods for Active Classification of Remote Sensing Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2015年第3期12卷 527-531页

作者： Bencherif, Mohamed A. Bazi, Yakoub Guessoum, Abderrezak Alajlan, Naif Melgani, Farid AlHichri, Haikel Saad Dahlab Univ Fac Technol Dept Elect Blida 9000 Algeria King Saud Univ Coll Comp & Informat Sci Dept Comp Engn ALISR Lab Riyadh 11543 Saudi Arabia Univ Trento Dept Informat Engn & Comp Sci I-38213 Trento Italy

In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.

关键词： Active learning (AL) extreme learning machine (ELM) graph-based optimization multiclass classification

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Super-process interoperability optimization architecture in healthcare ultra-large-scale systems: A graph-based multi-objective approach

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第3期34卷

作者： Shirazi, Babak Mazandaran Univ Sci & Technol Dept Ind Engn Babol Iran

The healthcare industry is more challenging and the super-process interoperability should be planned in order to architecture an informational technology structure of the required data for progression of care. Large-scale enterprises could be provided cross-organization super-processes operations. Therefore, each enterprise's business process management system (BPMS) may be executed cross-organizational BPM as a kind of the process model. This study proposed a Super-process Interoperable Optimized Architecture based on BPMS as a paradigm to satisfy the consistency of cross-organization operations in healthcare industry. For developing the healthcare ultra-large-scale process management system (H-ULS-PMS), the paper provided interoperability optimization with intelligent recommender system in estimation component of BPMS that has been altered to H-ULS-PMS. This system could be recommended the appropriate concept of crossed healthcare process according to the hospitals' requirements. A graph-based multi-objective optimization method has been applied to control distributed resources across the hospitals. The community cloud is used as proposed architecture infrastructure for largely scaled interoperability. A practical case in hospital as a research project of a hospital in Dusseldorf-Wersten of Germany has been studied to show the better interoperability and resource management of the proposed super-process interoperability architecture.

关键词： graph-based optimization healthcare ultra-large-scale process management system interoperability resource management super-process interoperable optimized architecture

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Evolutionary algorithm with a regression model for multiobjective minimization of systemic risk in financial systems

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SOFT COMPUTING 2024年第5期28卷 3921-3939页

作者： Michalak, Krzysztof Wroclaw Univ Econ & Business Dept Informat Technol Wroclaw Poland

This paper addresses a problem of systemic risk minimization in which the optimization algorithm has to simultaneously minimize the number of companies affected by a wave of bankruptcies simulated on a graph as well as the level of reserves the companies keep to avoid going bankrupt. A MOEA/D-NN algorithm (where NN stands for a neural network) is proposed, which optimizes parameters of a machine learning model (a neural network) used in turn to determine the level of reserves the companies keep, based on several attributes describing each node in the graph. In the experiments, the proposed MOEA/D-NN algorithm was found to outperform comparison methods: evolutionary algorithms optimizing the level of reserves for all companies and a method based on the training of neural networks on a dataset previously collected by an evolutionary algorithm solving "training" instances of the optimization problem. The neural networks optimized by MOEA/D-NN were also tested on problem instances based on REDS graphs generated using varying values of R, E, and S parameters and were found to be applicable to these instances for certain ranges of parameters. The R parameter controlling the possibility of generating long-distance connections was found to have a bigger impact on the performance of the optimized neural networks than the other two parameters.

关键词： graph-based optimization Neural networks Systemic risk mitigation Multiobjective optimization

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Accurate and Robust Visual Localization System in Large-Scale Appearance-Changing Environments

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第6期27卷 5222-5232页

作者： Yu, Yang Yun, Peng Xue, Bohuan Jiao, Jianhao Fan, Rui Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Dept Elect & Comp Engn Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res I Shenzhen Peoples R China

Localization in large-scale environments with robust performance is a persistent challenge for mobile robots. This article proposes a novel system to achieve accurate and robust visual localization performance in largescale environments with appearance-changing surroundings. Our system starts from a stage of extracting stable visual features with an object segmentation network. After measurement postprocessing and extrinsic precalibration, we propose a graph-based optimization module to estimate the optimal pose as well as extrinsics. We construct optimization constraints with refined wheel odometry, feature matching between images, and correspondences between images and the prebuild map. We evaluate our segmentation module on our proposed datasets and test our localization module with seven sequences (9.8 km total length) in real port scenes with different working conditions from day to night and sunny to rainy. Experiment results demonstrate the decimeter-level accuracy and robust performance of our approach in various challenging scenarios, showing competitive performance compared with state-of-theart LiDAR-based localization methods.

关键词： graph-based optimization neural network online calibration visual localization

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Evolutionary Neural Networks for Livestock Disease Prevention

Evolutionary Neural Networks for Livestock Disease Preventio...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Giacobini, Mario Wroclaw Univ Econ & Business Dept Informat Technol Wroclaw Poland Univ Torino Dept Vet Sci Grugliasco TO Italy

ISBN: (纸本)9798400701207

In this article, multiobjective optimization of neural models is studied, aimed at making decisions about vaccine distribution in a scenario of a disease spreading among farms, pastures and other locations. The epidemic is simulated using a real-life dataset of animal movements between such premises in Italy in years 2017-2020. Three neural models are studied: multilayer perceptrons (MLP), classical recurrent neural networks (RNN) and Long Short-Term Memory (LSTM) networks with the weights of the networks optimized using the MOEA/D algorithm. In the experiments, MOEA/D with the binary-vector representation optimizing the assignment of vaccinations to the premises was used as a comparison method. In order to assess the generalization capacity of the tested methods, the optimization was performed on data from the years 2017-2019 and the results (optimized neural models, and optimized vaccination assignments, respectively) were used for the year 2020. The two neural models which worked best were the MLP and the LSTM which outperformed both the RNN and MOEA/D optimizing the assignment of vaccinations. The advantage of the MLP and the LSTM is particularly well-visible when the number of vaccinated nodes is low, which may be important in practical applications.

关键词： epidemics control zoonotic diseases multiobjective optimization graph-based optimization

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Optimized variable resource allocation framework for scheduling of fast-track industrial construction projects

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AUTOMATION IN CONSTRUCTION 2024年 158卷

作者： Taghaddos, Maedeh Mousaei, Ali Taghaddos, Hosein Hermann (Rick), Ulrich Mohamed, Yasser Abourizk, Simaan Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada Univ Tehran Coll Engn Sch Civil Engn Tehran Iran PCL Ind Management Inc Engn Dept Edmonton AB Canada Univ Alberta 5-080 NREF Edmonton AB T6G 2W2 Canada

Efficient scheduling and resource allocation for large-scale industrial projects is challenging due to their size and complexity, especially with fast-track contracts, which often lack detailed information during the early planning phase. This paper introduces a data-driven workface planning framework to enhance scheduling and resource allocation while accommodating uncertainties and constraints (e.g., minimum and maximum resource allocation curves, dynamic predecessor relationships, congestion limits). This framework employs an integrated approach, combining time-stepped simulation with graph-based optimization. By leveraging historical data and expert knowledge, the data-driven framework mitigates certain subjective assumptions, including durations and resource allocations. In practical application, the framework generates near-optimal schedules, even with limited information. Applying the framework to a fast-track industrial construction project case study demonstrated enhanced resource allocation. These findings offer practical benefits to industrial projects regarding time and cost savings and serve as a foundation for future research in data-driven project planning approaches.

关键词： Scheduling Flexible resource allocation graph-based optimization Workface planning Data-driven time-stepped simulation Space management Industrial construction Fast-track contracts

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Research on SLAM Algorithm of Mobile Robot based on the Fusion of 2D LiDAR and Depth Camera

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IEEE ACCESS 2020年 8卷 157628-157642页

作者： Mu, Lili Yao, Pantao Zheng, Yuchen Chen, Kai Wang, Fangfang Qi, Nana Anhui Univ Sci & Technol Sch Mech Engn Huainan 232001 Peoples R China Univ Sydney Sch Math & Stat Sydney NSW 2006 Australia

This paper proposes a new Simultaneous Localization and Mapping (SLAM) method on the basis of graph-based optimization through the combination of the Light Detection and Ranging (LiDAR), RGB-D camera, encoder and Inertial Measurement Unit (IMU). It can conduct joint positioning of four sensors by taking advantaging of the unscented Kalman filter (UKF) to design the related strategy of the 2D LiDAR point cloud and RGB-D camera point cloud. 3D LiDAR point cloud information generated by the RGB-D camera under the 2D LiDAR has been added into the new SLAM method in the sequential registration stage, and it can match the 2D LiDAR point cloud and the 3D RGB-D point cloud by using the method of the Correlation Scan Matching (CSM);In the loop closure detection stage, this method can further verify the accuracy of the loop closure after the 2D LiDAR matching by describing 3D point cloud. Additionally, this new SLAM method has been verified feasibility and availability through the processes of theoretical derivation, simulation experiment and physical verification. As a result, the experiment shows that the multi-sensor SLAM framework designed has a good mapping effect, high precision and accuracy.

关键词： Simultaneous localization and mapping Mobile robots Laser radar Three-dimensional displays Wheels SLAM RGB-D graph-based optimization multi-sensor fusion

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Solving the Parameterless Firefighter Problem using Multiobjective Evolutionary Algorithms 19

Solving the Parameterless Firefighter Problem using Multiobj...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Michalak, Krzysztof Wroclaw Univ Econ Inst Business Informat Dept Informat Technol Wroclaw Poland

ISBN: (纸本)9781450367486

The Firefighter Problem (FFP) is a graph-based optimization problem that is an abstraction of real-life problems such as epidemics control, economic crises prevention, etc. In the FFP spreading of fire is simulated on a graph in discrete time steps. In the original formulation of the problem a fixed number of graph nodes N-f can be defended in each time step. In this paper the problem is reformulated, and three different solution representations are studied. In one of the representations (N+P), the N-f parameter is a decision variable and in the other two (P using permutations and T using integer vectors) it is determined when the solution is decoded. Because higher N-f values mean more resources used for defense it is desirable to minimize this value, but on the other hand we want to minimize the number of graph nodes consumed by fire. Therefore the Parameterless FFP is tackled using two well-known multiobjective evolutionary algorithms: the MOEA/D and the NSGA-II as a multiobjective optimization problem with two and three objectives. The results presented in the paper show that for the Parameterless FFP the best solution representation is N+P.

关键词： graph-based optimization Solution representation Combinatorial optimization MOEA/D NSGA-II

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Mobile robot localization algorithm based on multi-sensor information fusion

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Journal of Measurement Science and Instrumentation 2020年第2期11卷 152-160页

作者： WANG Ming-yi HE Li-le LI Yu SUO Chao College of Mechanical&Electrical Engineering Xi’an University of Architecture&TechnologyXi’an 710055China

In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was *** this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo *** feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction *** graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual *** results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified.

关键词： mobile robot simultaneous localization and mapping(SLAM) graph-based optimization sensor fusion

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